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EPRI’S WATERSHED ANALYSIS RISK MANAGEMENT FRAMEWORK (WARMF) VS. USEPA’S BETTER ASSESSMENT SCIENCE INTEGRATING POINT AND NONPOINT SOURCES (BASINS)

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Pp. 460-470 in Total Maximum Daily Load (TMDL) Environmental Regulations–II Proceedings of the 8-12 November 2003 Conference (Albuquerque, New Mexico USA), Publication Date 8 November 2003.  .(doi:10.13031/2013.15597)
Authors:   B. T. Neilson, J.S. Horsburgh, D.K. Stevens, M.R. Matassa, and J.N. Brogdon
Keywords:   WARMF, BASINS, WinHSPF, watershed modeling, decision support systems, TMDLs, watershed management

There are numerous water quality modeling packages available from industry and government that assist in watershed decision-making and total maximum daily load (TMDL) development. Uncertainty exists among decision makers concerning the appropriateness of these tools and modeling packages to specific TMDL issues. Tennessee Valley Authority (TVA), in collaboration with Utah State University and the Electric Power Research Institute (EPRI), has undertaken a comparison of Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Analysis Risk Management Framework (WARMF). BASINS, developed by the USEPA, and WARMF, developed by EPRI, are watershed management decision support systems that integrate data, geographic information systems (GIS), and models. Although similar in their description, there are important differences in model setup requirements, technical expertise requirements, overall modeling approaches, and the application of model results to watershed decision making and TMDL development.

The portion of this study presented in this paper compares the requirements, strengths, and weaknesses of WARMF with BASINS for general watershed management and TMDL activities. Specifically, guidance is provided on DSS selection by providing information on the capabilities of each system, human resource requirements, and the approximate costs associated with model setup and calibration. Overall, it was found that each system has strengths and weaknesses and that choosing one system over another is dependent on many factors, including in-house modeling expertise, constituents to be modeled, the number of watershed modeling efforts required, and available funds.

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